miRNome profiling of lung cancer metastases revealed a key role for miRNA-PD-L1 axis in the modulation of chemotherapy response

Locally advanced non-small cell lung cancer (NSCLC) is frequent at diagnosis and requires multimodal treatment approaches. Neoadjuvant chemotherapy (NACT) followed by surgery is the treatment of choice for operable locally advanced NSCLC (Stage IIIA). However, the majority of patients are NACT-resistant and show persistent lymph nodal metastases (LNmets) and an adverse outcome. Therefore, the identification of mechanisms and biomarkers of NACT resistance is paramount for ameliorating the prognosis of patients with Stage IIIA NSCLC. Here, we investigated the miRNome and transcriptome of chemo-naïve LNmets collected from patients with Stage IIIA NSCLC (N = 64). We found that a microRNA signature accurately predicts NACT response. Mechanistically, we discovered a miR-455-5p/PD-L1 regulatory axis which drives chemotherapy resistance, hallmarks metastases with active IFN-γ response pathway (an inducer of PD-L1 expression), and impacts T cells viability and relative abundances in tumor microenvironment (TME). Our data provide new biomarkers to predict NACT response and add molecular insights relevant for improving the management of patients with locally advanced NSCLC. Supplementary Information The online version contains supplementary material available at 10.1186/s13045-022-01394-1.


Background
Lung cancer is frequently diagnosed as advanced-stage disease (Stage III-IV) with metastases spread to regional and distant organs in more than two-thirds of cases [1]. Despite the progress made in early diagnosis and treatment, the prognosis of patients remains poor with 5-year survival rates ranging from 32 to 6%, depending on the presence of regional or distant metastases, respectively [1]. One-third of patients with non-small cell lung cancer (NSCLC), i.e., the most common type of lung cancer (~ 80-85% of cases), are diagnosed with locally advanced disease (Stage III). Stage III disease is heterogeneous both for tumor size (from < 3 cm, T1; to > 7 cm, T4) and for metastatic spreading (i.e., regional lymph nodes, N2-N3; ipsilateral peribronchial and/ or ipsilateral hilar lymph nodes and intrapulmonary † Tommaso Colangelo and Juliana Guarize 15:178 nodes, N1) [2]. Stage IIIA-N2 disease is prevalent and, when resectable, is preferentially treated by neoadjuvant chemotherapy (NACT; platinum-based doublet (P-doublet)) before surgery to target nodal metastases and reduce/eradicate metastatic disease. Indeed, NACT is an effective treatment in N2 patients improving the overall survival by 5% at 5 years [3]. However, clinical responses to NACT differ widely, ranging from patients achieving complete eradication of all nodal metastases at the time of surgery (pN0) to patients having a persistent metastatic disease (pN+) [4][5][6], which suggests the presence of different molecular features among and within nodal metastatic lesions, as recently described also in other studies [7,8]. Recently, the combination of immune checkpoint inhibitors (ICI) targeting the PD-1/PD-L1 axis (i.e., Nivolumab) with P-doublet chemotherapy in the neoadjuvant setting, showed improved clinical management of patients with resectable NSCLC [9] and gained approval by Food and Drug Administration (FDA). In addition, other ongoing clinical trials are also evaluating the efficacy of ICI alone or in combination with NACT for stage IIIA-N2 NSCLC patients [10]. Nevertheless, the current scant knowledge of the molecular biology of metastases makes it difficult to search for cancer driver mechanisms alongside the development of predictive biomarkers and new druggable targets.
Here, by exploring the miRNA-mRNA transcriptional network of lung cancer lymph node metastases in stage IIIA-N2 disease, we derived miRNA signatures predictive of NACT response. Importantly, using in vitro and in vivo lung cancer models, we showed for the first time the role of miR-455-5p in mediating chemotherapy resistance and immune evasion by means of PD-L1 expression regulation.

Lung cancer metastatic cells exhibit a distinct miRNA profile according to their sensitivity to NACT
We initially investigated the molecular profile of tumor metastatic cells from mediastinal lymph nodes (i.e., LNmets; station 4 and 7; see method) collected by endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) before NACT in treatment naïve stage IIIA patients who had a complete pathological response (pN0; n = 5) or with persistent disease (pN2; n = 7) after P-doublet NACT (i.e., EBUS samples; Table 1). LNmets were expanded in cell culture (Fig. 1A) as we previously showed [11]; morphological examination together with immunofluorescence staining using anti-pan-cytokeratin antibody (Pan-CK) confirmed their epithelial origin (Fig. 1B). Yet, LNmets were enriched in the expression of typical markers of cells constituting the airway epithelium (NKX2-1, KRT5, CC10, SOX2, SFTPC; Fig. 1C). Next, we performed highthroughput microRNA expression profiling of LNmets by TaqMan Low-density Array (TLDA; see Methods) and we detected a total of 197 miRNAs (Cqn < 30.01 in at least 50% of samples for group; see Methods) ( Fig. 1D-E; Additional file 1: Data File 1). Overall, many miRNAs were downregulated in patients who developed pN2 disease (n = 87, 44.9%; FC < 0.67) (Fig. 1F), with 16 miRNAs (aka, LN-signature) statistically significant (p < 0.05) ( Fig. 1F-G). TLDA analysis of LNmets in a second independent FFPE cohort of stage III patients (n = 52) collected by mediastinoscopy (i.e., MED samples; Table 2; Additional file 5: Fig. S1A; see Methods) resulted in the detection of 170 miRNAs (Additional file 5: Fig. S1B), largely overlapping with those identified in EBUS samples (Additional file 5: Fig. S1C) and with a comparable expression level (Additional file 5: Fig. S1D). Again, we observed a general loss of miRNA expression in patients who developed pN2 disease (Additional file 5: Fig. S1E-F). Unsupervised clustering analysis using the LN-signature discriminated pN0 from pN2 also in this independent cohort of patients ( Fig. 2A), while partial responder patients (pN1), in line with their intermediate phenotype, resulted to be scattered along the cluster ( Fig. 2A). Notably, MED samples showed a similar epithelial cell content as in EBUS samples though with a stronger expression of markers of the tumor microenvironment (TME) (CDH5, PTPRC aka CD45, and ACTA-2) (Fig. 2B) which, on the contrary, were absent in pure epithelial LNmets (EBUS samples). Yet, 12 out of the 16 miRNAs of the original LN-signature were also found differentially expressed in MED samples (pN2 vs. pN0; p < 0.05) ( Fig. 2C and Additional file 5: Fig. S1G). Ridge-penalized logistic regression using the LN-signature (16-miRNA model) resulted in a perfect separation of responders and non-responders in the EBUS cohort when used as a training set, which slightly decreased in the MED cohort used as a validation set (AUC = 0.76) (Fig. 2D-E, Additional file 17: Table S1). When only miRNAs detected in MED samples were used (14-miRNA model), the model reached an AUC = 0.82 in the validation set ( Fig. 2D and F, Additional file 17: Table S1). Lastly, as small numbers of biomarkers are easier to use in the clinical practice, we applied LASSO regression which identified a signature of 4 miRNAs (4-miRNA model) with an AUC of 0.81 in the validation set ( Fig. 2D and G, Additional file 17: Table S1). Importantly, the clinical model alone, built by combining all available clinical and pathological parameters, showed an AUC of 67% in the validation set which increased up to 82% when combined with miRNA-based risk models (Table 3). Collectively, these results showed a distinct pattern of miRNA expression in LNmets which is predictive of chemotherapy response.

Functional analysis of predictive microRNAs to NACT response
We then used the LN-signature to identify mechanisms of chemotherapy resistance. First, we analyzed public drug screening datasets, such as CTRPv2, GDSC1-2 and PRISM [12][13][14][15][16], to retrieve cisplatin (i.e., the backbone component of NACT) sensitivity data in NSCLC cell lines for which miRNA expression data were available (CCLE dataset). Unexpectedly, the cytotoxic effect of cisplatin was negligible in the majority of the cell lines at the indicated doses (Fig. 3A, Additional file 18: Table S2). However, we noticed that, at least in the GDSC2 dataset, DMSO was used as a compound vehicle, which is known to rapidly inactivate cisplatin [17]. Therefore, we performed a small-scale drug screening to test cisplatin sensitivity (dissolved in NaCl 0.9%) of a panel of metastatic NSCLC cell lines. Cells were treated with increasing doses of cisplatin and drug sensitivity was measured by sigmoidal curve fitting (Fig. 3B). NSCLC cell lines exhibited a heterogeneous sensitivity profile to cisplatin, with potency (IC 50 ) ranging from 1.5 to 11 µM and efficacy (E max ) calculated at the peak plasma concentration of cisplatin upon injection (Cmax, ~ 12 µM: [18,19]) from 0 to 0.5 relative cell viability (Fig. 3C). When we analyzed the expression of our LN-signature in chemo-naïve NSCLC cell lines, we observed a variable degree of association between IC 50 /E max values and miRNAs expression (Fig. 3D) 3F). We also scored a negative correlation for miR-140-3p (IC 50, r = −0.76 p = 0.037; E max , r = −0.69 p = 0.069) whose overexpression was indeed shown to sensitize NSCLC cells to cisplatin [20,21] (Fig. 3D).

miR-455-5p regulates cisplatin resistance of lung cancer metastatic cells
Next, we investigated whether miR-455-5p was sufficient to modulate the chemotherapy response of NSCLC cells. To this end, we took advantage of the NCI-H1993 cell line which i) was derived from LNmets of a stage IIIA NSCLC patient, ii) is a miR-455-5p lowexpressing cell line and iii) has a higher resistance to cisplatin (Fig. 3E). NCI-H1993 cells were transfected with a miR-455-5p mimic (OE) or a negative mimic control (CTRL) and the increased levels of miR-455-5p after overexpression were confirmed by qRT-PCR  4A). Importantly, we observed that miR-455-5p OE in NCI-H1993 strongly increased sensitivity to cisplatin ( Fig. 4B) with a significant decrease in cisplatin potency in comparison with CTRL cells (Fig. 4C). We then investigated whether miR-455-5p could play a role also in acquiring cisplatin resistance and thus we treated the cisplatin sensitive NCI-H2023 cell line ( Fig. 3C) with increasing doses of cisplatin during cycles of drug on (4 days) and drug off (1-2 weeks) (Fig. 4D). Long-term treatment with cisplatin resulted in the generation of  Table S3A). Lastly, we observed a reduced proliferation rate and a higher migratory/invasive capability of CDDP-R cells (Additional file 6: Fig.  S2J-L).
In line with the above observations, miR-455-5p was significantly downregulated after the acquisition  of cisplatin resistance in CDDP-R vs. parental cells (Fig. 4E). We, therefore, transfected miR-455-5p in both parental and CDDP-R cells (Fig. 4F) and performed cell viability analysis upon cisplatin treatment (Fig. 4G). Strikingly, miR-455-5p overexpression in CDDP-R cells induced cisplatin sensitivity in terms of both potency and efficacy when compared to parental cells or parental cells overexpressing miR-455-5p ( Fig. 4G-H), thus suggesting a specific miR-455-5p-addiction in resistant cells.
We validated such findings also in vivo by using a zebrafish cell-derived xenograft (zCDX) model which was recently shown to be valuable in oncology research [23,24]. First, parental and CDDP-R cells overexpressing miR-455-5p or not, as a control, were fluorescently labeled and then injected into the perivitelline space of zebrafish larvae (Fig. 4I). qRT-PCR analysis confirmed miR-455-5p OE before cell inoculation (Additional file 7: Fig. S3A). Next, zebrafish embryos were treated with cisplatin at a dose near Cmax (~ 16 µM) and tumor growth was analyzed ( Fig. 4I-J). The implantation rate was 100% in both cell lines upon injection (on day 0), with parental cells that formed slightly smaller tumors when compared to tumors formed by CDDP-R cells (Additional file 7: Fig. S3B-C). The cisplatin treatment induced a significant reduction in the tumor size of the parental tumors but not of the CDDP-R ones ( Fig. 4K-L).   Strikingly, miR-455-5p overexpression re-sensitized CDDP-R tumors to cisplatin ( Fig. 4K-L). Yet, miR-455-5p OE alone caused a significant reduction in the tumor burden in CDDP-R untreated resistant tumors ( Fig. 4K-L). This is in line with in vitro data where miR-455-5p OE impaired tumor cell proliferation (Additional file 8: Fig. S4A-B) and with the observation that high miR-455-5p expressing tumors from TGCA-LUAD cohort are smaller in size when compared to low miR-455-5p ones (Additional file 20: Table S4).

PD-L1 is a direct molecular link between miR-455-5p and cisplatin resistance
We then asked which molecular mechanisms can be influenced by miR-455-5p and their role in cisplatin resistance. To tackle this, we reconstructed miRNA-mRNA transcriptional networks by performing transcriptome analysis of LNmets (MED samples) which identified 1702 differentially expressed genes (DEGs) (fold change >|1.5|; p < 0.05) in pN2 vs. pN0 patients (Fig. 5A). GSEA using a curated gene set representing miR-455-5p-predicted target genes (n = 349, Additional file 3: Data File 3; see Methods) revealed a positive enrichment (FDR < 0.05) of miR-455-5p targets in pN2 patients, which was coherent with the previously observed loss of miR-455-5p expression (Fig. 5B). Next, we used the 'Hallmark genes set' collections in GSEA which revealed a number of pathways involved in the regulation of proliferation, metabolism, immune evasion, development and response to cellular stresses, enriched in LNmets of pN2 patients (FDR < 0.05) (Fig. 5C, Additional file 19: Table S3B). To functionally dissect the regulation of pN2-enriched pathways, we transfected NCI-H1993 and CDDP-R cells with a miR-455-5p mimic (OE) or a negative mimic control (CTRL) and performed transcriptome analysis. GSEA confirmed the modulation of miR-455-5p target genes upon miRNA overexpression (  Table S3B-D). Next, we looked among genes belonging to IFN-α and IFN-γ response pathways to search for putative miR-455-5p target genes by TargetScan analysis [25]. BATF2, CMPK2, IRF2, MYD88, SOCS3 and PD-L1 (aka CD274) genes were all predicted to be targeted by miR-455-5p (Fig. 5F). Among these genes, PD-L1 expression was previously reported to be found increased after NACT treatment in NSCLC [26][27][28]. Moreover, besides the well-known role of PD-L1 in the regulation of T cell activity through the interaction with the receptor PD-1, it was also found to regulate critical functions of cancer cells in a cell-autonomous way, including chemotherapy resistance [29,30]. Therefore, we speculated that miR-455-5p regulation would impact chemotherapy response through PD-L1 direct regulation. Overall, we analyzed PD-L1 expression (mRNA, total and cell surface protein) in our panel of NSCLC cell lines (Additional file 9: Fig.  S5A-C) and found that a higher expression of PD-L1 was associated with cisplatin resistance (Additional file 9: Fig. S5D-E). Furthermore, when we silenced PD-L1 expression by siRNAs in NCI-H1993 cells the sensitivity to cisplatin increased significantly (Additional file 9: Fig.  S5F-H). Conversely, the acquisition of cisplatin resistance was accompanied by a concomitant increase in PD-L1 expression in CDDP-R when compared to parental cells (Additional file 10: Fig. S6A-C). Accordingly, silencing of PD-L1 by siRNAs in CDDP-R cells (Additional file 10: Fig.  S6D) was able to strongly enhance cisplatin sensitivity when compared to control cells (Additional file 10: Fig.  S6E-F), whilst no effect was scored in the parental cell lines where PD-L1 expression was low (Additional file 10: Fig. S6E-F).
To test the direct effect of miR-455-5p on PD-L1 expression regulation, we took advantage of customdesigned oligonucleotides (target site blockers; TSBs) that specifically prevent the binding of miR-455-5p to the PD-L1 3′-UTR. Transfection of TSBs in CDDP-R cells rescued PD-L1 loss of expression upon miR-455-5p OE (Fig. 6H). Strikingly, the rescue of PD-L1 expression upon TSB transfection resulted in the recovery of cisplatin resistance of CDDP-R/miR-455-5p OE cells (Fig. 6I), thus suggesting that miR-455-5p regulates cisplatin response in a PD-L1-dependent manner.

miR-455-5p overexpression decreases T cell apoptosis
The interaction of PD-L1 with its cognate receptor PD-1 inhibits the proliferation and activation of T cells [36]. Therefore, we asked ourselves whether miR-455-5pdependent PD-L1 regulation in tumor cells may impact T cells viability. To this purpose, we took advantage of Jurkat cells, a leukemic T cell line widely used in the literature for T cell signaling studies [41]. NCI-H1975 cells (miR-455-5p OE or CTRL) were co-cultured for 72 h with Jurkat cells in the presence of CD3/CD28/ CD2 soluble antibody complexes to induce activation and PD-1 expression on the T cell surface (Fig. 7A). Strikingly, miR-455-5p OE decreased the percentage of apoptotic T cells when compared to T cells co-cultured with NCI-H1975 CTRL cells (Fig. 7B-C; Additional file 11: Fig. S7A). Likewise, we observed a significant reduction in apoptotic T cells when we directly silenced PD-L1 in NCI-H1975 (Fig. 7B-C; Additional file 11: Fig. S7A). Next, we analyzed the correlation of miR-455-5p expression with CD8 T cell infiltration in two independent cohorts of primary NSCLC tumors (the CSS and CIMA-CUN cohorts; Additional file 22: Table S6; Fig. 7D, E). The analysis revealed a positive correlation between miR-455-5p expression and the percentage of CD8 T cells in high tumor-infiltrating lymphocytes (TILs) tumors (Fig. 7D, E). Strikingly, when we performed a pooled analysis (n = 47) by combining the two cohorts, we confirmed that a higher level of miR-455-5p was associated with a higher infiltration of CD8 T cells (Fig. 7F). Furthermore, we leveraged the TCGA-LUAD and TCGA-LUSC datasets to grasp further information about CD8 T cells subsets infiltration in NSCLC samples high-/low-miR-455-5p expressing: (i) TCGA samples were stratified in 'High, ' 'Int' and 'Low' miR-455-5p expressing samples (see Methods); (ii) PD-L1 expression likewise expression signatures related to CD8exhausted T cells [42] and of IFN response were analyzed in High/Int/Low miR-455-5p tumor subsets ( Fig. 7G; see Methods). Strikingly, the expression levels of miR-455-5p were inversely correlated to signatures of enriched exhausted CD8 + T cell (aka GET) and of IFN response (Fig. 7G) in LUAD tumors, thus further reinforcing the link among miR-455-5p, PD-L1 and impact on T cells viability. Lastly, the analysis of the distribution of 'Immune Subtypes' introduced by Thorsson et al. [43] revealed, in LUAD low-miR-455-5p expressing samples, a depletion of the 'inflammatory subtype (C3) (enriched in proinflammatory T helper Th1 and Th17 cells) which enhances CD8 + T cells cytotoxicity (Fig. 7G). Contrariwise, the miR-455-5p expression had no effects on the immune subtypes of LUSC tumors which, by and large, showed a distinct immune composition in comparison with LUAD tumors due to the predominance of the C2 subtype and the absence of the C3 subtype (Fig. 7G). Notably, when we analyzed N2 metastasis by the CIBERSORTx algorithm [44], we found that pN2 MED samples were characterized by a trend in the reduction in cytotoxic cells, such as NK-activated cells and T cell CD8, which was in line with our previous observations (Additional file 14: Fig. S10A, Additional file 23: Table S7). Moreover, pN2 and pN0 metastases were also characterized by varying expression levels of MHC and immune-inhibitors molecules (Additional file 14: Fig. S10B).
Overall, these data suggest that miR-455-5p-dependent inhibition of PD-L1 expression may affect CD8 T cell phenotype, thus improving T cell antitumor immune response. . P values were calculated by one sample t test. *P < 0.05, **P < 0.01, ***P < 0.001; ns, not significant. H Representative flow cytometry histogram plots (left) and quantification (right) of cell surface PD-L1 MFI in CDDP-R cells transfected with a miR-455-5p mimic or a negative control in the presence of a scramble TSB or a PD-L1-specific miR-455-5p TSB. Data are reported as fold change in MFI relative to CDDP-R cells transfected with a CTRL miRNA mimic and with a scramble TSB. Data are mean ± SEM (N = 6). P values were calculated by one sample t test. *P < 0.05, **P < 0.001; ns, not significant. I Bar plot representing cell viability (Fold change relative to CTRL mimic in the presence of a scramble TSB) of CDDP-R cells transfected as in (G) and treated for 72 h with cisplatin at the indicated doses. Data are mean ± SEM (N = 5). P values were calculated by one sample t test. *P < 0.05, **P < 0.01; ns, not significant. J Bar plot representing cell surface PD-L1 expression in the indicated cell lines stimulated for 48 h with ± 40 ng/ml of IFN-γ. The result is shown as fold change in the MFI relative to Parental CTRL cells. Data are mean ± SEM (N = 3). P values were calculated by one sample t test. *P < 0.05, **P < 0.01, ***P < 0.001; ns, not significant. K Immunoblot analysis of pEGFR, EGFR and PD-L1 in BEAS-2B transfected with a miR-455-5p mimic or a negative control and treated for 36 h with ± 40 ng/ml of EGF. GAPDH was used as loading control. L Immunoblot analysis of pEGFR, EGFR and PD-L1 expression in NCI-H1975 transfected with a miR-455-5p mimic or a negative control. GAPDH was used as loading control

Discussion
Patients with locally advanced lung cancer treated by NACT in combination with surgery had better survival than patients treated by surgery alone, in randomized trials [45]. However, the response rate to NACT is still suboptimal due to the clinical and biological heterogeneity of lung tumors. Recent improvements have been made by introducing the use of ICI (e.g., nivolumab, pembrolizumab and atezolizumab [46][47][48]) in combination with cisplatin-based chemotherapy, to trigger the immune response against primary and metastatic lung cancer lesions [49]. Yet, the prediction of chemo/immunotherapy response as well as the identification of mechanisms of resistance in metastatic lung cancer patients is still an unmet need [50]. In recent years, microRNAs have emerged as master regulators of critical processes for lung cancer onset and progression [51]. Their role in driving lung cancer was found to be overall exerted through the expression regulation of targeted cancer-driver genes [51] and the modulation of complex cancer epigenetic mechanisms which impact tumor cells fitness by, for example, inducing EMT [52], stemness [53], immune evasion [54], and resistance to chemotherapy [55]. Furthermore, the exceptional stability of miRNA in harsh conditions and their presence in the body fluids [56] make them ideal candidates for the development of diagnostic, prognostic and predictive biomarkers [57].
Here, we performed a transcriptome analysis (miRNA and mRNA profiling) of LNmets of a cohort of patients with stage IIIA lung tumors by molecular profiling of EBUS and mediastinoscopy samples. We showed that N2 metastases resistant to NACT were characterized by an overall loss of miRNAs expression consistent with their prevalent role as tumor suppressors [58], as well as a profound reshape of the coding transcriptome. Our identified miRNA-based signatures (aka LN-signature) were accurate enough to predict NACT response which, to our knowledge, are the first of this kind and will warrant further investigations in larger and multicentric cohorts of patients.
Importantly, we unveiled that the miR-455-5p/PD-L1 axis regulates the chemotherapy response of NSCLC cells, hallmarks metastases with active IFN-γ response pathway (an inducer of PD-L1 expression [34]), and impacts T cells viability and relative abundances in TME (Fig. 7H). Remarkably, when we investigated the expression profile of miR-455-5p and correlated it with cisplatin sensitivity metrics, we found that the loss of expression of miR-455-5p hallmarked intrinsic chemoresistance of NSCLC cell lines. This was in line with the miR-455-5p regulation in EBUS and MED samples which strongly suggested the relevance of miR-455-5p in controlling mechanisms of intrinsic and acquired chemoresistance. Indeed, we showed that miR-455-5p OE was sufficient to restore cisplatin sensitivity both in vitro and in vivo.
Several mechanisms involving drug accumulation, drug efflux and mediators of response to DNA damage have been implicated in platinum resistance so far [59]. Recently, PD-L1 was shown to regulate intracellular functions of cancer cells in a cell-autonomous way besides its immune-suppressive role on the membrane, including the regulation of cisplatin resistance [29,30]. NSCLC tumors treated with chemotherapy express higher levels of PD-L1 which, in turn, correlate with resistance and poor prognosis [26,27,60]. In keeping with this, we observed that PD-L1 expression is increased in resistant cells (both at basal level and upon cisplatin treatment) and direct inhibition of PD-L1 expression sensitizes cells to cisplatin treatment. Importantly, we found that miR-455-5p directly targets PD-L1 in lung  miR-455-5p expression has been found dysregulated in several human malignancies including colon cancer, hepatic cancer, NSCLC, gastric cancer and prostate cancer [62][63][64][65]. Recently, a work by Chen et al. has reported that miR-455-5p is able to regulate cisplatin resistance in bladder cancer via the HOXA-AS3-miR-455-5p-Notch1 axis [66]. However, in our study, neither the HOXA-AS3 nor the NOTCH1 expressions were found modulated upon miR-455-5p OE in vitro or in N2 metastases (Additional file 15: Fig. S11A-B). As a matter of fact, we noticed that the miR-455-5p overexpression resulted in either minor or no effect on cisplatin sensitivity in low PD-L1 expressing cells, thus highlighting the role of PD-L1 as a central mediator of the miR-455-5p activity in the context of drug resistance in NSCLC. A recent study suggested that miR-455-5p could target PD-L1 3'UTR in hepatocellular carcinoma cells [67]. However, the validation of the miRNAbinding site in the PD-L1 gene was carried out only in an unphysiological context (e.g., luciferase-based assay) and was not even confirmed in a real-world cohort of patients. Moreover, no data were presented about the role of miR-455-5p/PD-L1 axis in the regulation of cisplatin response and cancer immune evasion.
The binding of tumor PD-L1 with the receptor PD-1 on T cells activates a signaling cascade that alters the T cell activity in many ways, including the inhibition of T cell proliferation and survival, cytokine production and other effector functions [36]. Therefore, we expect that miR-455-5p-PD-L1 axis may have also a role in a non-cell-autonomous way by regulating cancer immune evasion in LNmets of stage IIIA patients. As a matter of fact, we showed that LNmets, which express low level of miR-455-5p, are characterized by a higher amount of both PD-L1 and PD-1 mRNA together with a trend of reduction in CD8 T cells, as we predicted in silico by CIBERSORTx analysis. Although an immunohistochemistry (IHC) analysis of LNmets to measure PD-L1, PD-1 and T cell markers was not feasible due to limited amount of samples, we showed in primary NSCLC tumors that a higher level of miR-455-5p was associated with decreased PD-L1 expression and increase in CD8 + T cell infiltration, in line with our hypotheses. Recently, FDA-approved neoadjuvant nivolumab plus p-doublet chemotherapy in resectable NSCLC regardless of PD-L1 tumor status [9]. Although PD-L1 expression modulation was associated with immunotherapy response [68], PD-L1 has not been considered as reliable biomarkers mainly due to its spatial and temporal heterogeneous expression [69] with PD-L1 negative tumors which responded also to ICIs [70]. However, GSEA analysis revealed that N2 metastases were enriched in a set of genes belonging to IFN-γ signature. IFN-γ is a proinflammatory cytokine produced by T cell and NK cells and is able to increase PD-L1 levels in cancer cells, thus promoting the inhibition of the T cell activity in the TME. Moreover, IFN-γ-related gene signatures have been recently reported to predict the response to anti-PD-1 therapy in melanoma [71] and NSCLC patients [72]. Interestingly, our data indicate that miR-455-5p overexpression in vitro is able to decrease both IFN-γ-mediated PD-L1 expression and the enrichment in IFN-γ related genes observed in resistant cells, which deserves further investigations to explore the role of miR-455-5p and the overall LN-signature as potential reliable biomarkers to predict the response to ICIs. Moreover, given the ability of miRNA-based LN-signature to accurately predict NACT response, such signature could also be exploited in future studies as a potential biomarker for the newly approved drug regimen based on ICIs plus NACT.
Further studies have also highlighted a high tumor heterogeneity between metastatic lesions and primary tumors in the same NSCLC patients in terms of both pathway activation and PD-L1 expression [73], which may impact chemotherapy and immunotherapy response.
Although a direct comparison between nodal metastases and primary tumors was unfeasible in our cohorts, our data represent an important step forward in understanding the molecular mechanisms driving chemoresistance in lung cancer metastatic cells. Furthermore, we provided evidence for an unedited contribution of the miR-455-5p-PD-L1 axis in the regulation of chemoresistance and immunoevasion at the level of lymph nodal metastases, thus adding new grounds for bringing chemo-immunotherapy a step closer to stage IIIA clinical practice.

Conclusions
Here, we showed that treatment naïve LNmets were characterized by distinct miRNA expression patterns which were predictive of NACT response. Importantly, by coupling whole miRNA and mRNA profiling, we unveiled a key role for the miR-455-5p/PD-L1 axis which regulates chemotherapy response and immune evasion in metastatic NSCLC cells. To our knowledge, our study represents the most comprehensive transcriptome (coding and non-coding) analysis of LNmets in NSCLC patients. In conclusion, we described novel miRNAbased biomarkers and unveiled relevant mechanisms for LNmets resistance to chemotherapy which will contribute to improving the outcome of lung cancer patients.

Tumor sample collection and processing EBUS samples
Samples were obtained and processed as previously described [11]. EBUS-TBNA samples were collected from the mediastinal LNs station 4 and 7 of patients using a convex probe (EBUS Convex Probe BF-UC180F; Olympus), a dedicated ultrasound processor (EU-ME2; Olympus) and a 22-gauge dedicated needle (Vizishot NA-201SX-4022; Olympus). One dedicated needle passage was put into the cell culture medium for primary cell culture expansion. Briefly, EBUS-TBNA samples were centrifuged for 5 min at 1000 g at RT, resuspended in a complete medium [11] and cultured on collagen-I rat tail (Gibco) coated plates for 6 to 12 days prior to total RNA extraction (

MED samples
Two FFPE tissue sections (5-10 μm thick) on glass slides with adequate tumor cellularity (> 60%) were selected by a certified pathologist and microdissected by scraping with a scalpel prior to RNA isolation as previously described [11] (Additional file 18: Table S2). Criteria for selection of patients were: i) pathologically confirmed stage IIIA-pN2 NSCLC; ii) not having been treated before for their disease; iii) suitability for NACT followed by surgery.  Table S5 and Additional file 22: Table S6. NCI-H2023, NCI-H1993, NCI-H1975, NCI-H838,  NCI-H1944, NCI-H1437, NCI-H1573, NCI-H2126, NCI-H322M, BEAS-2B and Jurkat were obtained from ATCC and cultured in RPMI (Gibco) with 5% FBS, 1% penicillin/streptomycin except for Jurkat medium, which was supplemented with 10% FBS. Primary cell cultures from LNmets of stage IIIA NSCLC were obtained and maintained as previously described [11]. All cell lines were grown at 37 °C in a humidified incubator with 5% CO 2 and routinely tested for Mycoplasma contamination using PCR.

Creation of cisplatin-resistant cells (CDDP-R)
Cisplatin (P4394, Sigma-Aldrich) was dissolved in vehicle solution (NaCl 0.9%) at a final concentration of 1 mg/ml and stored in the dark at RT for a maximum of 28 days. NCI-H2023 cells were subjected to treatment cycles (n = 11), consisting of 3-4 days of cisplatin treatment and 1-2 weeks of culture in RPMI 5% FBS 1% penicillin/ streptomycin to allow survived cells (i.e., the CDDP-R) to proliferate. The dose at the first treatment cycle was 0.6 μM and then increased in subsequent cycles until reaching a maximum dose of 10 μM. Parental cells treated with vehicle solution were cultured in parallel and used as a control.

Cell viability assay
Cells were seeded in 96-well plates in triplicate in 90 μL of complete media. On day 1 post-seeding, cells were treated with increasing doses of cisplatin (threefold serial dilution), or vehicle solution as a control. Cell viability was assessed by adding CyQUANT Cell Proliferation Assay Kit (Life Technologies) in a ratio of 1:10 directly in complete media. Fluorescence was measured at 480/528 nm using a Sinergy HT (Biotek) microplate reader and IC 50 was estimated using the online tool GR calculator [74].

Quantitative real time-PCR (qRT-PCR) of miRNAs and mRNAs
For qRT-PCR of miRNAs, 10 ng of total RNA was reverse-transcribed using a TaqMan MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific) and RT-specific primers for miRNAs (Thermo Fisher Scientific, See Additional file 25:  Table S9) and QuantStudio 12 k Flex thermocycler (Thermo Fisher Scientific) using the manufacturer's recommended cycling conditions. Data were normalized using the geometric mean of three genes (ESD1, GUSB and HPRT) as reference. Data normalization for both miRNAs and mRNAs was performed by using the deltadelta CT method or the calculation of the normalized Cq as previously described [75]. according to the manufacturer's cycling conditions and by setting an automatic threshold. Cq data of miRNAs were normalized (Cqn) using U6 snRNA as previously described [75]. miRNAs with a Cq < 30.01 in at least 50% of samples among one of the experimental groups tested in the analysis were considered as detected.

Zebrafish cell-derived xenograft (zCDX)
zCDX models were developed by a CRO (BioReperia AB). Transgenic Tg(fli1:EGFP)y1 zebrafish embryos were raised at 28 °C for 48 h in E3 embryo medium (containing per liter: 0.286 g NaCl, 0.048 g CaCl2, 0.081 g MgSO4 and 0.0126 g KCl with 0.2 mM 1-Phenyl-2-Thiourea aka PTU). At 2 days post-fertilization, embryos were injected subcutaneously in the perivitelline space with transfected parental and CDDP-R cells previously labeled with FAST Dil ™ oil (Thermo Fisher Scientific) and treated with ± Cisplatin 5 mg/L for 3 days. Images of tumors were taken by using a fluorescent stereoscope with a K5 camera (Leica) and LAS X software v3.7.1.21655 at 100× magnification with no binning. Images of tumors were taken right after injection (day 1) and after drug treatment (day 4). Images were automatically analyzed by using the HuginMunin software v2.7.0.0 (BioReperia AB). Tumor growth regression was calculated by dividing the number of tumor pixels on day 4 by the number of tumor pixels on day 1 in the same embryo and multiplied by 100.

Genome-wide expression profiling
Gene expression profiling of MED samples and NSCLC cell lines (two independent biological replicates) was carried out using the GeneChip ® Pico reagent Kit and the GeneChip ® WT Plus reagent Kit, respectively. For both reagents, the GeneChip ® Human Clarion S Array (Thermo Fisher Scientific) was used according to the manufacturer's instructions. Quality control, normalization of CEL files and statistical analysis were performed using the Transcriptome Analysis Console (TAC) software v4.0 (Thermo Fisher Scientific) by performing the 'Gene level SST-RMA' summarization method with human genome version hg38. Differentially expressed genes were defined as those with a fold change (FC) difference of at least 1.5 and a p value less than 0.05. For MED samples, 5 pN2 and 5 pN0 samples balanced for age, sex and histotype were pooled to obtain 2 pools for each experimental condition (pN2 and pN0). Microarray expression data can be found in the GEO database (GSE193707).

Predictive risk model
A ridge-penalized unconditional logistic regression was applied in the training set to model the odds of responding as a function of the 16 miRNAs that were scored as differentially expressed between responder and non-responder patients in the EBUS samples (16-miRNA model). The same strategy was used for the 14-miRNA and 4-miRNA models. Cross-validated (tenfold) loglikelihood with optimization (50 simulations) of the tuning penalty parameter was applied. The probability of being a responder was estimated, and model performance was assessed using the area under the receiver operating curve (AUC). Min-max scaling of miRNAs expression in the validation set was implemented before applying the predictive model. LASSO approach was used to reduce the number of predictors.

Analysis of cell line publicly available datasets
Cell viability of cisplatin for the indicated dataset was downloaded directly from the DepMap portal (https:// depmap. org/ portal/ compo und/ cispl atin? tab= dosecurves). Analysis of cell viability data was restricted only to NSCLC cell lines for which miRNA expression data was available in the CCLE dataset. Median cell viability was calculated at each concentration and plotted. Quality control (QC) for IC 50 estimation was applied following instructions reported in Sebaugh et al. [76]. Briefly, we estimate IC 50 values for cell lines in each dataset by taking advantage of cell viability data downloaded from DepMap portal and the online software 'GR calculator. ' QC criteria applied were: at least two concentrations below the 50% response concentration and above the 50% response. Only proportions of cell lines in each dataset for which IC 50 estimation was accurate according to Sebaugh et al. were reported (see Additional file 18: Table S2).

CIBERSORTx analysis
CIBERSORTx [44] was run using the online Web tool (https:// ciber sortx. stanf ord. edu) and following the developers' instructions. The CIBERSORTx analysis was conducted using the following settings: LM 22 as signature matrix file, absolute mode running and 100 permutations. CIBERSORTx score is an estimation of cell fraction of each specific subpopulation in each tumor sample. CIBERSORTx complete results were reported in Additional file 23: Table S7.

Gene set enrichment analysis (GSEA)
GSEA (GSEA, https:// www. gsea-msigdb. org/ gsea/ index. jsp) was performed using Signal2Noise metric, 1000 random sample sets permutation, and median gene expression values for class comparison. For enrichment analysis of hallmarks of cancer, we used the gene matrix h.all.v7.4 symbols.gmt available from MSigDB. For miR-455-5p target enrichment analysis, we built a custom gene matrix by including human genes that were highly or moderately predicted to be miR-455-5p targets (cumulative weighted context + + score ≤ −0.2) by TargetScan (release 7.2) and were well expressed (log 2 intensity > 4) in all samples used in each analysis. Significant gene sets were considered as those with a false-discovery rate (q value) of less than 5%. For singlesample gene set enrichment analysis of TGCA cohorts, ssGSEA scores were calculated by using the GSVA package in R. Gene signatures for exhausted CD8 + T cell were obtained from Cai et al. [42], while gene signatures for IFN-γ and IFN-α response were downloaded from MSigDB hallmark gene sets (version h.all.v7.4 symbols. gmt).

Statistics
Hierarchical clustering was performed using Cluster 3.0 (C Clustering Library